{"total":609,"rows":[{"id":"2201.05624","title":"Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next","created":"2022-01-14","cited_by_count":608,"area":null},{"id":"2107.03502","title":"CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation","created":"2021-07-07","cited_by_count":582,"area":"diffusion"},{"id":"2310.07820","title":"Large Language Models Are Zero-Shot Time Series Forecasters","created":"2023-10-11","cited_by_count":500,"area":null},{"id":"2107.05847","title":"Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges","created":"2021-07-13","cited_by_count":145,"area":null},{"id":"2207.04179","title":"Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling","created":"2022-07-09","cited_by_count":127,"area":null},{"id":"2505.23719","title":"TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning","created":"2025-05-29","cited_by_count":120,"area":null},{"id":"2203.12742","title":"Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders","created":"2022-03-23","cited_by_count":102,"area":null},{"id":"2102.11409","title":"On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty","created":"2021-02-22","cited_by_count":87,"area":null},{"id":"2201.11872","title":"Local Latent Space Bayesian Optimization over Structured Inputs","created":"2022-01-28","cited_by_count":79,"area":null},{"id":"2307.14338","title":"TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023","created":"2023-07-26","cited_by_count":74,"area":null},{"id":"2305.17535","title":"PFNs4BO: In-Context Learning for Bayesian Optimization","created":"2023-05-27","cited_by_count":73,"area":null},{"id":"2202.06985","title":"Deep Ensembles Work, But Are They Necessary?","created":"2022-02-14","cited_by_count":63,"area":null},{"id":"2302.04237","title":"Black Box Adversarial Prompting for Foundation Models","created":"2023-02-08","cited_by_count":62,"area":"safety"},{"id":"2310.06743","title":"Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks","created":"2023-10-10","cited_by_count":61,"area":null},{"id":"2206.04771","title":"Joint Entropy Search for Maximally-Informed Bayesian Optimization","created":"2022-06-09","cited_by_count":55,"area":null},{"id":"2102.07188","title":"Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces","created":"2021-02-14","cited_by_count":53,"area":null},{"id":"2106.01413","title":"Rectangular Flows for Manifold Learning","created":"2021-06-02","cited_by_count":50,"area":null},{"id":"2109.06325","title":"safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics","created":"2021-09-13","cited_by_count":50,"area":null},{"id":"2203.16481","title":"On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification","created":"2022-03-30","cited_by_count":46,"area":null},{"id":"2111.01186","title":"Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces","created":"2021-11-01","cited_by_count":45,"area":null},{"id":"2210.02905","title":"Joint Entropy Search for Multi-objective Bayesian Optimization","created":"2022-10-06","cited_by_count":45,"area":null},{"id":"2212.04450","title":"GAUCHE: A Library for Gaussian Processes in Chemistry","created":"2022-12-06","cited_by_count":41,"area":null},{"id":"2106.11899","title":"Local policy search with Bayesian optimization","created":"2021-06-22","cited_by_count":38,"area":null},{"id":"2104.10873","title":"Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains","created":"2021-04-22","cited_by_count":37,"area":null},{"id":"2110.15486","title":"DOCKSTRING: easy molecular docking yields better benchmarks for ligand design","created":"2021-10-29","cited_by_count":35,"area":null},{"id":"2206.02063","title":"Active Bayesian Causal Inference","created":"2022-06-04","cited_by_count":35,"area":null},{"id":"2405.03658","title":"A review on data-driven constitutive laws for solids","created":"2024-05-06","cited_by_count":35,"area":null},{"id":"2402.02746","title":"Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization","created":"2024-02-05","cited_by_count":34,"area":null},{"id":"2208.11866","title":"NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators","created":"2022-08-25","cited_by_count":33,"area":null},{"id":"2103.01439","title":"Fast Adaptation with Linearized Neural Networks","created":"2021-03-02","cited_by_count":32,"area":null},{"id":"2106.12997","title":"Bayesian Optimization with High-Dimensional Outputs","created":"2021-06-24","cited_by_count":32,"area":null},{"id":"2110.13361","title":"A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs","created":"2021-10-26","cited_by_count":32,"area":null},{"id":"2111.01732","title":"Spatio-Temporal Variational Gaussian Processes","created":"2021-11-02","cited_by_count":32,"area":null},{"id":"2210.10953","title":"Discovering Many Diverse Solutions with Bayesian Optimization","created":"2022-10-20","cited_by_count":31,"area":null},{"id":"2302.03020","title":"RLSbench: Domain Adaptation Under Relaxed Label Shift","created":"2023-02-06","cited_by_count":30,"area":null},{"id":"2107.00363","title":"Valid prediction intervals for regression problems","created":"2021-07-01","cited_by_count":29,"area":null},{"id":"2210.12496","title":"Bayesian Optimization with Conformal Prediction Sets","created":"2022-10-22","cited_by_count":28,"area":null},{"id":"2307.00618","title":"Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces","created":"2023-07-02","cited_by_count":28,"area":null},{"id":"2206.01558","title":"Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning","created":"2022-06-03","cited_by_count":27,"area":null},{"id":"2306.11589","title":"Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent","created":"2023-06-20","cited_by_count":27,"area":null},{"id":"2310.17273","title":"Looping in the Human Collaborative and Explainable Bayesian Optimization","created":"2023-10-26","cited_by_count":27,"area":null},{"id":"2205.10186","title":"Bayesian Active Learning with Fully Bayesian Gaussian Processes","created":"2022-05-20","cited_by_count":26,"area":null},{"id":"2206.12569","title":"Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels","created":"2022-06-25","cited_by_count":25,"area":null},{"id":"2210.15452","title":"Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity","created":"2022-10-20","cited_by_count":25,"area":null},{"id":"2311.03721","title":"ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning","created":"2023-11-07","cited_by_count":25,"area":null},{"id":"2105.12909","title":"Deconditional Downscaling with Gaussian Processes","created":"2021-05-27","cited_by_count":24,"area":null},{"id":"2111.01460","title":"Geometry-aware Bayesian Optimization in Robotics using Riemannian Mat\\'ern Kernels","created":"2021-11-02","cited_by_count":24,"area":null},{"id":"2205.15449","title":"Posterior and Computational Uncertainty in Gaussian Processes","created":"2022-05-30","cited_by_count":24,"area":null},{"id":"2303.01774","title":"Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings","created":"2023-03-03","cited_by_count":24,"area":null},{"id":"2106.03195","title":"Meta-Learning Reliable Priors in the Function Space","created":"2021-06-06","cited_by_count":23,"area":null},{"id":"2107.00243","title":"Preconditioning for Scalable Gaussian Process Hyperparameter Optimization","created":"2021-07-01","cited_by_count":23,"area":null},{"id":"2110.15172","title":"Conditioning Sparse Variational Gaussian Processes for Online Decision-making","created":"2021-10-28","cited_by_count":23,"area":null},{"id":"2202.11987","title":"Predicting the impact of treatments over time with uncertainty aware neural differential equations","created":"2022-02-24","cited_by_count":23,"area":null},{"id":"2207.00167","title":"Rethinking Optimization with Differentiable Simulation from a Global Perspective","created":"2022-06-28","cited_by_count":23,"area":null},{"id":"2208.00306","title":"Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation","created":"2022-07-30","cited_by_count":23,"area":null},{"id":"2406.04739","title":"A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences","created":"2024-06-07","cited_by_count":23,"area":null},{"id":"2206.03314","title":"Integrating Random Effects in Deep Neural Networks","created":"2022-06-07","cited_by_count":22,"area":null},{"id":"2304.11005","title":"Self-Correcting Bayesian Optimization through Bayesian Active Learning","created":"2023-04-21","cited_by_count":22,"area":null},{"id":"2106.06529","title":"The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective","created":"2021-06-11","cited_by_count":21,"area":null},{"id":"2205.06342","title":"Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning","created":"2022-05-12","cited_by_count":21,"area":null},{"id":"2502.00330","title":"From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation","created":"2025-02-01","cited_by_count":21,"area":"lr"},{"id":"2102.08314","title":"Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients","created":"2021-02-16","cited_by_count":20,"area":null},{"id":"2105.06964","title":"BNNpriors: A library for Bayesian neural network inference with different prior distributions","created":"2021-05-14","cited_by_count":20,"area":null},{"id":"2111.10144","title":"Positional Encoder Graph Neural Networks for Geographic Data","created":"2021-11-19","cited_by_count":20,"area":null},{"id":"2206.04734","title":"Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination","created":"2022-06-09","cited_by_count":20,"area":null},{"id":"2211.02732","title":"Multi-Fidelity Cost-Aware Bayesian Optimization","created":"2022-11-04","cited_by_count":20,"area":null},{"id":"2306.12306","title":"Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift","created":"2023-06-21","cited_by_count":20,"area":null},{"id":"2111.03412","title":"Dual Parameterization of Sparse Variational Gaussian Processes","created":"2021-11-05","cited_by_count":19,"area":null},{"id":"2103.01454","title":"Kernel Interpolation for Scalable Online Gaussian Processes","created":"2021-03-02","cited_by_count":18,"area":null},{"id":"2210.04994","title":"Sampling-based inference for large linear models, with application to linearised Laplace","created":"2022-10-10","cited_by_count":18,"area":null},{"id":"2210.13937","title":"Multi-Fidelity Bayesian Optimization with Unreliable Information Sources","created":"2022-10-25","cited_by_count":18,"area":null},{"id":"2312.00267","title":"Sample Efficient Preference Alignment in LLMs via Active Exploration","created":"2023-12-01","cited_by_count":18,"area":"alignment"},{"id":"2409.04538","title":"Operator Learning with Gaussian Processes","created":"2024-09-06","cited_by_count":18,"area":null},{"id":"2107.04619","title":"Diverse Video Generation using a Gaussian Process Trigger","created":"2021-07-09","cited_by_count":17,"area":null},{"id":"2109.02606","title":"Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications","created":"2021-09-06","cited_by_count":17,"area":null},{"id":"2112.01049","title":"Bayesian Optimization over Permutation Spaces","created":"2021-12-02","cited_by_count":17,"area":null},{"id":"2410.09385","title":"Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models","created":"2024-10-12","cited_by_count":17,"area":null},{"id":"2102.06695","title":"Bias-Free Scalable Gaussian Processes via Randomized Truncations","created":"2021-02-12","cited_by_count":16,"area":null},{"id":"2102.07542","title":"High-Dimensional Gaussian Process Inference with Derivatives","created":"2021-02-15","cited_by_count":16,"area":null},{"id":"2112.02206","title":"Data Fusion with Latent Map Gaussian Processes","created":"2021-12-04","cited_by_count":16,"area":null},{"id":"2211.06149","title":"Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization","created":"2022-11-11","cited_by_count":16,"area":null},{"id":"2306.00312","title":"(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy","created":"2023-06-01","cited_by_count":16,"area":null},{"id":"2306.09803","title":"Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization","created":"2023-06-16","cited_by_count":16,"area":null},{"id":"2310.19450","title":"Hodge-Compositional Edge Gaussian Processes","created":"2023-10-30","cited_by_count":16,"area":null},{"id":"2403.09875","title":"Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting","created":"2024-03-14","cited_by_count":16,"area":null},{"id":"2407.17033","title":"Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference","created":"2024-07-24","cited_by_count":16,"area":"diffusion"},{"id":"2104.11667","title":"Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure","created":"2021-04-23","cited_by_count":15,"area":null},{"id":"2107.04061","title":"Scaling Gaussian Processes with Derivative Information Using Variational Inference","created":"2021-07-08","cited_by_count":15,"area":null},{"id":"2110.06777","title":"Incremental Ensemble Gaussian Processes","created":"2021-10-13","cited_by_count":15,"area":null},{"id":"2202.09774","title":"Supervising the Multi-Fidelity Race of Hyperparameter Configurations","created":"2022-02-20","cited_by_count":15,"area":null},{"id":"2305.12086","title":"Prefix Propagation: Parameter-Efficient Tuning for Long Sequences","created":"2023-05-20","cited_by_count":15,"area":null},{"id":"2306.11715","title":"Multi-Fidelity Active Learning with GFlowNets","created":"2023-06-20","cited_by_count":15,"area":null},{"id":"2306.14809","title":"Tanimoto Random Features for Scalable Molecular Machine Learning","created":"2023-06-26","cited_by_count":15,"area":null},{"id":"2502.06601","title":"Amortized In-Context Bayesian Posterior Estimation","created":"2025-02-10","cited_by_count":15,"area":null},{"id":"2107.12342","title":"Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning","created":"2021-07-26","cited_by_count":14,"area":null},{"id":"2202.00060","title":"SnAKe: Bayesian Optimization with Pathwise Exploration","created":"2022-01-31","cited_by_count":14,"area":null},{"id":"2203.07322","title":"Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation","created":"2022-03-14","cited_by_count":14,"area":null},{"id":"2207.00879","title":"Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces","created":"2022-07-02","cited_by_count":14,"area":null},{"id":"2208.12515","title":"Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations","created":"2022-08-26","cited_by_count":14,"area":null},{"id":"2311.00463","title":"Robust and Conjugate Gaussian Process Regression","created":"2023-11-01","cited_by_count":14,"area":null}],"next_cursor":"100"}